Initially, volumetric approaches were used to make high resolution voxel grids using deep learning for accurate surface reconstruction. VoxNet [1]} uses 3D convolution on voxel grids for the classification tasks but the approach is limited to a \( 32^3 \)  resolution.
However, the Octnet [2]} approach utilizes the convolution operation up to a level of \( 256^3 \)  using space partitioning by converting 3D voxel grids into small octree structures.
Based on Octnet, OctnetFusion [3]} takes low resolution TSDF volume and estimates higher resolution TSDF volume.
Similarly, other approaches like [4]} also use octrees instead of 3D volumes.
Depending on this concrete data structure, these approaches suffer from performance issue like memory and timing requirements when increasing the input resolution.
